A Better k-means++ Algorithm via Local Search


Silvio Lattanzi, Christian Sohler ;
Proceedings of the 36th International Conference on Machine Learning, PMLR 97:3662-3671, 2019.


In this paper, we develop a new variant of k-means++ seeding that in expectation achieves a constant approximation guarantee. We obtain this result by a simple combination of k-means++ sampling with a local search strategy. We evaluate our algorithm empirically and show that it also improves the quality of a solution in practice.

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